Abstract
The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system's current conditions and turning it into an applicable source of information to update the navigation filter. This article aims to provide an extensive survey of information-aided inertial navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, which would otherwise remain unobservable.
Original language | English |
---|---|
Article number | 8507418 |
Pages (from-to) | 1-18 |
Number of pages | 18 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 72 |
DOIs | |
State | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Extended Kalman filter (EKF)
- inertial navigation systems (INSs)
- inertial sensors
- model-based navigation
- nonholonomic constraints (NHCs)
- pseudomeasurements
- vehicle constraints
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering